Machine Learning Engineer

Job Title:

Executive – Machine Learning Ops. Engineer

Location:

Andheri (Mumbai)
Department: IT

Employment Type:

Full-Time
Industry - Pharmaceutical

Job Summary
We are seeking a skilled and detail-oriented  MLOps Engineer with 3-4 years of experience  to drive the deployment, monitoring, and operational excellence of AI agents and machine learning models within enterprise environments. The ideal candidate will be responsible for building robust CI/CD pipelines for AI systems, optimizing GPU workloads, managing hybrid infrastructure (on-premises and cloud), and ensuring performance, security, and compliance — particularly within a regulated pharma environment.
This role requires strong experience in product ionizing ML/GenAI solutions and ensuring scalability, reliability, and compliance of AI systems handling sensitive data.

Key Responsibilities
1. Model & AI Agent Deployment
Own end-to-end deployment of ML models and AI agents into production environments.
Manage model versioning, rollback strategies, and lifecycle management.
Ensure high availability, scalability, and reliability of deployed systems.
2. CI/CD for AI Systems
Design and implement CI/CD pipelines tailored for ML and GenAI workflows.
Automate model training, testing, validation, and deployment processes.
Integrate model testing frameworks, performance checks, and compliance gates into pipelines.
Enable seamless integration between development, staging, and production environments.
3. Infrastructure & GPU Optimization
Manage and optimize GPU-based workloads for model training and inference.
Monitor and improve compute utilization, cost efficiency, and latency.
Administer and maintain cloud (AWS/Azure/GCP) and/or on-prem infrastructure environments.
Support containerized deployments using Docker and Kubernetes.
4. Performance Monitoring & Observability
Implement infrastructure and model performance monitoring systems.
Track system health, latency, throughput, resource utilization, and failure rates.
Establish alerting, logging, and incident response processes.
Continuously improve system performance and reliability through proactive monitoring.
5. Vector Database & Data Infrastructure Management
Deploy and manage vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma).
Optimize indexing, embedding pipelines, and retrieval performance for GenAI applications.
Ensure high availability and backup strategies for AI data systems.
6. Security, Access Control & Compliance
Implement role-based access control (RBAC) and secure authentication mechanisms.
Ensure infrastructure and AI systems comply with pharma regulatory standards and internal governance policies.
Manage sensitive data securely, including encryption (at rest and in transit).
Support audit readiness and documentation for compliance reviews.

Technical Skills & Competencies
Strong programming skills in  Python  and scripting languages
Hands-on experience with  CI/CD tools  (Jenkins, Git Hub Actions, Git Lab CI, Azure Dev Ops, etc.)

Experience with  Docker, Kubernetes, Helm
Knowledge of  ML lifecycle tools  (MLflow, Kubeflow, Airflow, etc.)

Experience with  GPU optimization and distributed training frameworks
Familiarity with  cloud platforms (AWS/Azure/GCP)  and hybrid infrastructure
Experience managing  vector databases
Knowledge of  monitoring tools  (Prometheus, Grafana, ELK, Datadog, etc.)
Understanding of data security, encryption, compliance frameworks (GxP, HIPAA preferred)

Qualifications
BE in Data Science / Artificial Intelligence / Computer Science  or  MCA
Certification in  CI/CD, Machine Learning, or related Dev Ops technologies
3–4 years of experience in MLOps / Dev Ops for ML systems
Experience in the pharmaceutical or regulated industry (preferred)

Preferred Attributes
Strong problem-solving and troubleshooting skills
Ability to work independently and manage production-critical systems
Detail-oriented with strong documentation practices
Experience supporting enterprise GenAI applications
Good communication and cross-functional collaboration skills

What We Offer
Exposure to regulated pharma AI environments
Collaborative and innovation-driven culture
Free access to on-site gym facility to support employee wellness